Apparatus and method for profiling users

ABSTRACT

Provided is a process of profiling a user of a mobile computing device, the process including: obtaining a location history of a user, the location history being based on signals from a mobile computing device of the user; obtaining a location-attribute score of a location identified in, or inferred from, the location history; determining, with a computer, a user-attribute score based on the location-attribute score; and storing the user-attribute score in a user-profile datastore.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to user profiles and, morespecifically, to generating user profiles based on user location.

2. Description of the Related Art

User profiles are useful in a variety of contexts. For example,advertisers often purchase advertising based on a desire to reachpotential customers having particular attributes. Such advertisers oftenemploy user profiles to select when, where, or how the advertiserconveys their message. Similarly, market researchers may analyze userprofiles to better understand the market for a given good or servicebased on attributes of buyers of that good or service. In anotherexample, user profiles may be used to customize products or services,for instance by customizing a software application according to theprofile of a user of the software application.

User profiles, however, can be difficult to obtain, as users generallyhave little incentive to generate a profile of themselves for use byothers. Such a task can be tedious and unpleasant. Further, user'srecollection of their behavior over time can be unreliable.

SUMMARY OF THE INVENTION

The following is a non-exhaustive listing of some aspects of the presenttechniques. These and other aspects are described in the followingdisclosure.

In some aspects, the present techniques include a process of profiling auser of a mobile computing device, the process including: obtaining alocation history of a user, the location history being based on signalsfrom a mobile computing device of the user; obtaining alocation-attribute score of a location identified in, or inferred from,the location history; determining, with a computer, a user-attributescore based on the location-attribute score; and storing theuser-attribute score in a user-profile datastore.

Some aspects include a tangible, machine-readable, non-transitory mediumstoring instructions that when executed by a data processing apparatus,cause the data processing apparatus to perform operations including:obtaining a location history of a user, the location history being basedon signals from a mobile computing device of the user; obtaining alocation-attribute score of a location identified in, or inferred from,the location history; determining a user-attribute score based on thelocation-attribute score; and storing the user-attribute score in auser-profile datastore.

Some aspects include a system, including: one or more processors; andmemory storing instructions that when executed by the processors causethe processors to perform operations including: obtaining a locationhistory of a user, the location history being based on signals from amobile computing device of the user; obtaining a location-attributescore of a location identified in, or inferred from, the locationhistory; determining a user-attribute score based on thelocation-attribute score; and storing the user-attribute score in auser-profile datastore.

BRIEF DESCRIPTION OF THE DRAWINGS

The above-mentioned aspects and other aspects of the present techniqueswill be better understood when the present application is read in viewof the following figures in which like numbers indicate similar oridentical elements:

FIG. 1 shows an example of an environment in which a user profileroperates in accordance with some embodiments;

FIG. 2 shows an embodiment of a process for profiling a user;

FIG. 3 shows an embodiment of another process for profiling a user; and

FIG. 4 shows an embodiment of a computer system by which theabove-mentioned systems and processes may be implemented.

While the invention is susceptible to various modifications andalternative forms, specific embodiments thereof are shown by way ofexample in the drawings and will herein be described in detail. Thedrawings may not be to scale. It should be understood, however, that thedrawings and detailed description thereto are not intended to limit theinvention to the particular form disclosed, but to the contrary, theintention is to cover all modifications, equivalents, and alternativesfalling within the spirit and scope of the present invention as definedby the appended claims.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS

FIG. 1 shows an example of a computing environment 10 having a userprofiler 12 operative to generate user profiles to be stored in auser-profile datastore 14. In some embodiments, the user profiler 12generates (for example, instantiates or updates) the user profiles basedon location histories from mobile devices 16 and attributes ofgeographic locations stored in a geographic information system 18. Theresulting user profiles may reflect the attributes of the locationsvisited by users. The location histories may be conveyed via theInternet 20 to remote locations, and the user profiles may be used byadvertisement servers 22 to select advertisements for presentation tousers or for other purposes described below.

The profiles may characterize a variety of attributes of users. In oneillustrative use case, a location history may indicate that a userfrequently visits geographic locations associated with tourism, and theprofile of that user may be updated to indicate that the user frequentlyengages in tourism, which may be of interest to certain categories ofadvertisers. Or a user may spend their working hours in geographic areasassociated with childcare and residences, and based on their locationhistory, the profile of that user may be updated to indicate that theuser likely engages in childcare for children younger than school age.Other examples are described below.

Further, as explained in detail below, the attributes associated withgeographic locations may vary over time (for example, an area withcoffee shops and bars may have a stronger association with consumptionof breakfast or coffee in the morning, an association which weakens inthe evening, while an association with entertainment or nightlife may beweaker in the morning and stronger in the evening). User profiles may begenerated in accordance with the time-based attributes that predominatewhen the user is in a geographic area. And in some embodiments, userprofiles may also be segmented in time, such that a portion of a givenuser's profile associated with a weekday morning may have differentattributes than another portion of that user's profile associated with aweekend night, for instance.

The user profiles may be used by advertisers and others in aprivacy-friendly fashion, such that users are expected to tend to opt into sharing their location history. For example, the user profiles may beaggregated to identify geographic areas having a high density of aparticular type of user at a particular time of the week, such as asports stadium having a relatively large number of users associated withfishing as a hobby, or a children's soccer field in which a relativelylarge number of people associated with golfing as a hobby might tend toco-occur on weekend mornings. Such correlations may be presented toadvertisers or others without disclosing information by which individualusers can be uniquely identified. In other applications, user-specificinformation may be provided, for example, users who opt in to sharingtheir profiles may receive user-specific services or communicationsformulated based on the individual profile of that user.

Accounting for time when characterizing geographic areas is believed toyield relatively accurate characterizations of places, as the activitiesthat people engage in at a given location tend to depend strongly ontime of day and week. And for similar reasons, accounting for time whenprofiling users is expected to yield relatively accuratecharacterizations of the users. Generating profiles based on locationhistory further offers the benefit of profiling users without imposingthe burden of manually doing so on the users themselves, and usingattributes of geographic areas in which the user travels is expected toyield relatively privacy-friendly data about the user. That said, notall embodiments offer all, or any, of these benefits, as variousengineering and cost trade-offs are envisioned, and other embodimentsmay offer other benefits, some of which are described below.

As noted above, the user profiler 12 obtains data from the mobiledevices 16 and the geographic information system 18 to output userprofiles to the user-profile datastore 14 for use by the ad servers 22or for other purposes. Accordingly, these components are described inthis sequence, starting with inputs, and concluding with outputs.

The mobile devices 16 maybe any of a variety of different types ofcomputing devices having an energy storage device (e.g., a battery) andbeing capable of communicating via a network, for example via a wirelessarea network or a cellular network connected to the Internet 20. In somecases, the mobile devices 16 are handheld mobile computing devices, suchas smart phones, tablets, or the like, or the mobile devices may belaptop computers or other special-purpose computing devices, such as anautomobile-based computer (e.g., an in-dash navigation system). Themobile devices 16 may have a processor and a tangible, non-transitorymachine-readable memory storing instructions that provide thefunctionality described herein when executed by the processor. Thememory may store instructions for an operating system, special-purposeapplications (apps), and a web browser, depending upon the use case. Itshould be noted, however, that the present techniques are not limited tomobile devices, and other computing devices subject to geolocation mayalso generate data useful for forming user profiles. For instance,set-top boxes, gaming consoles, or Internet-capable televisions may begeolocated based on IP address, and data from user interactions withthese devices may be used to update user profiles, e.g., with userinteraction indicating a time at which a user was at the geolocationcorresponding to the device.

This software may have access to external or internal services by whichthe location of the mobile device may be obtained. For example, themobile device may have a built-in satellite-based geolocation device(for instance a global-positioning system, or GPS, device or componentsoperative to obtain location from other satellite-based systems, such asRussia's GLONASS system or the European Union's Galileo system). Inanother example, location may be obtained based on the current wirelessenvironment of the mobile device, for example by sensing attributes ofthe wireless environment (e.g. SSIDs of wireless hotspots, identifiersof cellular towers, and signal strengths) and sending those attributesto a remote server capable of identifying the location of the mobiledevice. In some embodiments, the location may be obtained based on anidentifier of a network node through which the mobile device connects tothe Internet, for example by geocoding an IP address of a wirelessrouter or based on a location of a cellular tower to which the mobiledevice is connected. The location may be expressed as a latitude andlongitude coordinate or an area, and in some cases may include aconfidence score, such as a radius or bounding box defining area withinwhich the device is expected to be with more than some thresholdconfidence.

From time to time, the location of the mobile devices 16 may be obtainedby the mobile devices. For example, when a user interacts with aspecial-purpose application, in some cases, the application may havepermission to obtain the location of the mobile device and report thatlocation to a third party server associated with the application, suchthat the location may be obtained by the user profiler 12 from the thirdparty server. In another example, the user may visit a website havingcode that obtains the current location of the mobile device. Thislocation may be reported back to the server from which the website wasobtained or some other third party server, such as an ad server for anaffiliate network, and location histories may be obtained from thisserver. In another example, locations of the mobile devices 16 may beobtained without the participation of the mobile device beyondconnecting to a network. For instance, users may opt in to allowing acellular service provider to detect their location based on cellularsignals and provide that location to the user profiler 12. Dependingupon how location is obtained, the location may be acquiredintermittently, for example at three different times during a day when auser launches a particular application, or relatively frequently, forexample by periodically polling a GPS device and reporting the location.In some cases, the location history may include locations obtained morethan one-second apart, more than one-minute apart, more than one-hourapart, or more, depending upon the use case.

Locations may be obtained in real time from mobile devices 16 by theuser profiler 12, or in some embodiments, location histories may beobtained. Each location history may include records of geographiclocations of a given mobile device and when the mobile device was ateach location. In some cases, a location history may include records oflocation over a relatively long duration of time, such as more than overa preceding hour, day, week, or month, as some modes of acquiringlocation histories report or update location histories relativelyinfrequently. A location history for a given mobile device may include aplurality (e.g., more than 10 or more than 100) location records, eachlocation record corresponding to a detected location of the mobiledevice, and each location record including a geographic location and thetime at which the mobile device was at the location. The locationrecords may also include a confidence score indicative of the accuracyof the detected location. Geographic locations may be expressed in avariety of formats with varying degrees of specificity, for example as alatitude and longitude coordinates, as tiles in a grid with which ageographic area is segmented (e.g., quantized), or in some other formatfor uniquely specifying places.

The geographic information system 18 may be configured to provideinformation about geographic locations in response to queries specifyinga location of interest. In some embodiments, the geographic informationsystem 18 organizes information about a geographic area by quantizing(or otherwise dividing) the geographic area into area units, calledtiles, that are mapped to subsets of the geographic area. In some cases,the tiles correspond to square units of area having sides that arebetween 10-meters and 1000-meters, for example approximately 100-metersper side, depending upon the desired granularity with which a geographicarea is to be described.

In some cases, the attributes of a geographic area change over time.Accordingly, some embodiments divide each tile according to time. Forinstance, some embodiments divide each tile into subsets of some periodof time, such as one week, one month, or one year, and attributes of thetile are recorded for subsets of that period of time. For example, theperiod of time may be one week, and each tile may be divided by portionsof the week selected in view of the way users generally organize theirweek, accounting, for instance, for differences between work days andweekends, work hours, after work hours, mealtimes, typical sleep hours,and the like. Examples of such time divisions may include a duration fora tile corresponding to Monday morning from 6 AM to 8 AM, during whichusers often eat breakfast and commute to work, 8 AM till 11 AM, duringwhich users often are at work, 11 AM till 1 PM, during which users areoften eating lunch, 1 PM till 5 PM, during which users are often engagedin work, 5 PM till 6 PM, during which users are often commuting home,and the like. Similar durations may be selected for weekend days, forexample 8 PM till midnight on Saturdays, during which users are oftenengaged in leisure activities. Each of these durations may be profiledat each tile.

In some embodiments, the geographic information system 18 includes aplurality of tile records, each tile record corresponding to a differentsubset of a geographic area. Each tile record may include an identifier,an indication of geographic area corresponding to the tile (which forregularly size tiles may be the identifier), and a plurality oftile-time records. Each tile-time record may correspond to one of theabove-mentioned divisions of time for a given tile, and the tile-timerecords may characterize attributes of the tile at different points oftime, such as during different times of the week. Each tile-time recordmay also include a density score indicative of the number of people inthe tile at a given time. In some embodiments, each tile-time recordincludes an indication of the duration of time described by the record(e.g. lunch time on Sundays, or dinnertime on Wednesdays) and aplurality of attribute records, each attribute record describing anattribute of the tile at the corresponding window of time during somecycle (e.g., weekly).

The attributes may be descriptions of activities in which users engagethat are potentially of interest to consumers of the user-profiledatastore 14. For example, some advertisers may be interested in whenand where users go to particular types of restaurants, when and whereusers play golf, when and where users watch sports, when and where usersfish, or when and where users work in particular categories of jobs. Insome embodiments, each tile-time record may include a relatively largenumber of attribute records, for example more than 10, more than 100,more than 1000, or approximately 4000 attribute records, depending uponthe desired specificity with which the tiles are to be described. Eachattribute record may include an indicator of the attribute beingcharacterized and an attribute score indicating the degree to whichusers tend to engage in activities corresponding to the attribute in thecorresponding tile at the corresponding duration of time. In some cases,the attribute score (or tile-time record) is characterized by a densityscore indicating the number of users expected to engage in thecorresponding activity in the tile at the time.

Thus, to use some embodiments of the geographic information system 18, aquery may be submitted to determine what sort of activities users engagein at a particular block in downtown New York during Friday evenings,and the geographic information system 18 may respond with the attributerecords corresponding to that block at that time. Those attributerecords may indicate a relatively high attribute score for high-enddining, indicating that users typically go to restaurants in thiscategory at that time in this place, and a relatively low attributescore for playing golf, for example. Attribute scores may be normalized,for example a value from 0 to 10, with a value indicating the propensityof users to exhibit behavior described by that attribute.

The user profiler 12 may join the location histories and tile recordsimplicated by locations in those location histories to generate userprofiles. Thus, users may be characterized according to the attributesof the places those users visit at the time the user visits thoseplaces. The generated user profiles may then be stored by the userprofiler 12 in the user-profile datastore 14, as described below. Tothis end, or others, some embodiments of the user profiler 12 includes alocation-history acquisition module 24, a location-attribute acquisitionmodule 26, and a user-attribute updater 28 operative to generate userprofiles.

The user profiler 12 may be constructed from one or more of thecomputers described below with reference to FIG. 4. These computers mayinclude a tangible, non-transitory, machine-readable medium, such asvarious forms of memory storing instructions that when executed by oneor more processors of these computers (or some other data processingapparatus) cause the computers to provide the functionality of the userprofiler 12 described herein. The components of the user profiler 12 areillustrated as discrete functional blocks, but it should be noted thatthe hardware and software by which these functional blocks areimplemented may be differently organized, for example, code or hardwarefor providing the this functionality may be intermingled, subdivided,conjoined, or otherwise differently arranged.

The illustrated location-history acquisition module 24 may be configuredto acquire location histories of mobile devices 16 via the Internet 20.The location histories may be acquired directly from the mobile devices16, or the location histories may be acquired from various thirdparties, such as a third-party hosting Web applications rendered on themobile devices 16, third parties hosting servers to which locationhistories are communicated by apps on the mobile devices 16, or thirdparties providing network access to the mobile devices 16, such ascellular service providers, for example. The location-historyacquisition module 24 may include a plurality of sub-modules forobtaining location histories from a plurality of different providers.These sub-modules may be configured to request, download, and parselocation histories from a respective one of the different providers viaapplication program interfaces provided by those providers. Thesub-modules may translate the location histories from the differentproviders, which may be in different formats, into a common format foruse in subsequent processing. Location histories may be acquiredperiodically, for example monthly, weekly, or hourly, or morefrequently.

The user profiler 12 of this embodiment further includes thelocation-attribute acquisition module 26. The module 26 may beconfigured to obtain attributes of locations identified based on thelocation histories acquired by the location history acquisition module24. For example, the module 26 may be configured to iterate through eachlocation identified by each location history and query the geographicinformation system 18 for attributes of those locations at the time atwhich the user was at the corresponding location. In some cases, thelocation-attribute acquisition module 26 may also request attributes ofadjacent locations, such as adjacent tiles, from the geographicinformation system 18 so that the user-attribute updater 28 candetermine whether a signal from a given tile is consistent with that ofsurrounding tiles for assessing the reliability of various indications.

The acquired location histories and location attributes may be providedby modules 24 and 26 to the user-attribute updater 28, which in someembodiments, is configured to generate user profiles based on this data.In some cases, the user-attribute updater 28 is operative to performportions of the processes of FIG. 2 or 3, described in detail below, andattach attributes of places visited by users to the profile of thoseusers. These profiles may be stored by the user attribute updater 28 inthe user-profile datastore 14.

The user profile datastore 14 may be operative to store user profilesand, in some embodiments, address queries for data in the user profiles.The illustrated user-profile datastore 14 includes a plurality ofuser-profile records, each record corresponding to the profile of agiven user or a given mobile device 16. Each user-profile record mayinclude an identifier of the record (which may be a value otherwiseuncorrelated with the identity of the user to enhance privacy), and anidentifier of the source or sources of the location histories from whichthe profile was created such that subsequent location histories can bematched with the profile (e.g. a account associated with aspecial-purpose application, a cell phone number, or some other value,which may be hashed to enhance user privacy).

Each user-profile record may also include a plurality of profile timerecords indicating attributes of the user profile at different timesduring some cycle of time (e.g., portions of the week or month, orduring other periods like those described above with reference to thegeographic information system 18). In some cases, the profile-timerecords may correspond to the same durations of time as those of thetime-tile records described above. Each profile-time record may includean indication of the duration of time being described (e.g. Thursday'sat dinnertime, or Saturday midmorning) and a plurality of profileattribute records, each profile attribute record indicating thepropensity of the corresponding user to engage in an activity describedby the attribute during the corresponding time of the profile-timerecord. The profile time records may allow tracking of when users tendto engage in a given activity (time of day, day of week, week of year).In some embodiments, the profile attribute records correspond to thesame set of attribute records described above with reference to thegeographic information system 18. Each profile-attribute record mayinclude an indication of the attribute being characterized (e.g.,attending a children's soccer game, having brunch at a fast-casualdining establishment, parent running errands, or shopping at a mall) anda score indicating the propensity of the user to engage in the activityat the corresponding time, such as a normalized value from 0 to 10. Theattribute records may further include a sample size, indicative of thenumber of samples upon which the attribute score is based, for weightingnew samples, and a measure of variance among these samples (e.g., astandard deviation) for identifying outliers.

As described below, the user-profile records may be used for a varietyof purposes. For example, advertisers operating ad servers 22 may submitto the user-profile datastore 14 a query identifying one of theuser-profile records, such as the above-mentioned hashed value of a useraccount number or phone number, and the user-profile datastore 14 mayrespond with the attributes of the corresponding user at the currenttime. In some embodiments, to further enhance user privacy, queries maybe submitted for a specific attribute to determine whether to serve anadvertisement corresponding to the attribute, or a query may request abinary indication of whether the attribute score is above a threshold.

In another example, the user-profile datastore 14 may be used by theuser profiler 12 to augment the records in the geographic informationsystem 18. For example, an index may be created for each attribute thatidentifies tiles where users having relatively strong scores (e.g. abovea threshold) for the respective attribute tend to co-occur at giventimes. These indices may correspond to heat maps (though no visualrepresentation need be created) indicating where, for example, usersinterested in golf, tend to be during various times of the day, suchthat advertisers can select advertisements based on this information. Insome embodiments, an index may be created for each user attribute ateach of the above-described divisions of time in the geographicinformation system 18, and these indices may be queried to providerelatively prompt responses relating to where users having a givenattribute or combination of attributes tend to co-occur at varioustimes. Precalculating the indices is expected to yield faster responsesto such queries than generating responsive data at the time the query isreceived. For instance, using examples of these indices relating tofishing and employment in banking, an advertiser may determine thatpeople who engage in fishing on the weekend and work in banking tend todrive relatively frequently along a particular stretch of road onMondays during the evening commute, and that advertiser may purchase anadvertisement for bass fishing boats on a billboard along that road inresponse. Other examples relating to customization of software andservices and other forms of analysis are described in greater detailbelow.

In short, some embodiments of the computing environment 10 generate userprofiles that are relatively privacy-friendly to users and consumerelatively little effort on the part of users or others to create theprofiles. These advantages are expected to yield a relativelycomprehensive set of relatively high-resolution user profiles that maybe used by advertisers and others seeking to provide information andservices customized to the unique attributes of each user, facilitatingthe presentation of high-value and high-relevance advertisements andservices to users while respecting the users' interest in privacy. Thatsaid, not all embodiments provide these benefits, and some embodimentsmay forgo some or all of these embodiments in the interest of variousengineering trade-offs relating to time, cost, and features.

FIG. 2 illustrates an embodiment of a process 30 that may be performedby the above-describes user profiler 12. The steps of the process 30(and other processes described herein) may be performed in a differentorder than the order in which the steps are described. In someembodiments, the process 30 includes obtaining a location history of auser, as illustrated by block 32. This step may be performed by theabove-described location-history acquisition module 24. As noted above,location histories may be obtained from a plurality of differentproviders of location histories, and the location histories may bereformatted into a common format for subsequent processing.

The process 30 of this embodiment further includes obtaining alocation-attribute score of a location identified in, or inferred from,the location history, as indicated by block 34. This step may beperformed by the above-described location-attribute acquisition module26. The location-attribute score may be one of a plurality of scorescorresponding to a time-tile record described above.

In some embodiments, locations identified in the location history may berelatively sparse, and intermediate locations between those explicitlyidentified may be inferred. For example, the user profiler 12 maydetermine that two locations are more than a threshold amount of timeapart and a threshold distance apart, indicating that the user likelytraveled between the location during the intermediate time. In response,the user profiler 12 may query the geographic information system 18 forlocations associated with travel, such as locations corresponding to aninterstate highway, between the two locations, and the locations alongthe interstate highway (or associated with some other mode of travel)may be added to the location history at the intermediate times asinferred locations. Inferring intermediate locations is expected toyield a more comprehensive characterization of the user's profile.

In some embodiments, the process 30 further includes determining auser-attribute score based on the location attribute score, as indicatedby block 36. Determining a user-attribute score may include incrementinga sample size for the corresponding attribute in the user profile andcalculating an updated average attribute score. An average is one of avariety of different forms of measures of central tendency that may beused to determine the user-attribute score. In other embodiments,previous attribute scores of locations visited by the user may be storedin memory, and a median or mode score may be calculated using the newlyobtained location-attribute score and those stored in memory. Thus,deviations indicating one-time instance in which the user engaged in aparticular activity will tend to have a relatively small effect on theuser profile, as previous location histories will likely indicate arelatively low propensity to engage in a particular activity and dilutethe effect of a single instance.

In some embodiments, the process 30 further includes storing theuser-attribute score in a user-profile datastore, as indicated by block38. As noted above, this may include updating indices corresponding tovarious attributes in a geographic information system, and the storeduser profiles may be queried by advertisers and others seeking toprovide targeted messaging and services. Targeting may be towardspecific users who are profiled or to the places profiled users visit orbased on patterns in attribute scores among profiled users.

FIG. 3 illustrates another embodiment of a process 40 for generatinguser profiles. This process may be performed by the above-mentioned userprofiler 12. The illustrated process begins with receiving a locationhistory of a user device from an application executed on the device, asillustrated by block 42. Next, intermediate locations are inferredbetween locations in the location history, as indicated by block 44. Asnoted above, some embodiments may identify gaps in the location historyand infer intermediate locations based on attributes of tiles betweenthe boundaries of the gap, selecting, for example, intermediate tilesassociated with transit. In this embodiment, the process 40 includesdetermining whether there are any un-analyzed locations in the locationhistory, as indicated by block 46. Upon determining that no suchlocations remain, the process completes. Alternatively, upon determiningthat locations remain to be analyzed, the process 40 in this embodimentproceeds to identify a time-bin corresponding to a time-stamp of thenext location in the location history, as indicated by block 48. Thetime-bin may be one of the above-describes durations of time by whichtiles or user profiles are characterized. The corresponding time bin maybe the time bin in which the timestamp of the use location falls. Next,this embodiment of process 40 includes querying a geographic informationsystem for tile records of the tiles corresponding to the location andadjacent tiles, as indicated by block 50.

In some embodiments, the process 40 further includes determining whetherthe user is likely at an adjacent location, as indicated by block 52.Such a determination may include making the determination based on theattributes of the adjacent tiles or density scores for the tilescorresponding to the timestamp of the user location in question. Forexample, attribute scores for the location in the location history mayindicate that less than a threshold amount of user activity occurswithin the tile corresponding to that location (e.g., a density valueindicative of the number of people in the tile may be below athreshold), while attribute scores for one of the adjacent tiles mayindicate a relatively high density or amount of activity (e.g., morethan a threshold, or more than a threshold difference from the adjacenttile) for one or more attributes. In response to this difference, it maybe determined that the location in the location history is in error (forinstance, due to an inaccurate GPS reading), and the adjacent locationmay be selected as being a more likely location of the user. Someembodiments may select the adjacent location having the highest densityor aggregation of attribute scores, for example. Thus, some embodimentsmay designate the adjacent location as the user location, as indicatedby block 54, or in response to a negative determination in block 52,some embodiments may proceed to the next step without such a designationoccurring.

Embodiments may further include determining whether adjacent tiles havesimilar location-attribute scores, as indicated by block 56. Becauselocation measurements by mobile devices are often relatively inaccurate,there is some risk that the user is not at the location identified andis instead in an adjacent tile. However, if the adjacent tiles havesimilar attribute scores, those attributes can be attributed to the userwith a relatively high degree of confidence regardless of whether theuser is in the exact tile identified by the location in the locationhistory. Accordingly, some embodiments may determine whether theadjacent tiles have similar location attribute scores (at the time inquestion for the user location), for example attribute scores less thana threshold difference for more than a threshold number of attributes.Other embodiments may calculate a confidence score based on thesimilarity of adjacent tiles and weight the modification of the userprofile based on the confidence score, down weighting signals ininstances in which the adjacent tiles are relatively different from oneanother, or a binary determination may be made as illustrated in FIG. 3.Upon determining that adjacent tiles do not have similarlocation-attribute scores, some embodiments return to block 46.Alternatively, the process may proceed to the next step.

Some embodiments of process 40 may include determining whether thelocation-attribute score is an outlier for the user, as indicated byblock 58. This step may include iterating through each locationattribute score of the user's location and comparing that attributescore at the time in question to a corresponding attribute score in theusers profile to identify uncharacteristic behavior indicative of apotentially misleading signal. In some embodiments, attributes may bedesignated as an outlier in response to the location attribute scoreexceeding a threshold difference from the average, for example locationattribute scores more than three standard deviations higher or lowerthan the average attribute score in the user profile for a givenattribute. In some embodiments, the determination of step 58 is made foreach of a plurality of attributes of the location, and those attributesdeemed to be outliers may be filtered before proceeding to the nextstep, or some embodiments may return to step 46 in response to thedetection of an outlier. Some implementations may use a similarity modelto detect inaccurate signals in acquired location histories. Using suchmodels, embodiments may filter out questionable location readings so asnot to pollute profile development. For instance, a reliability databasesimilar to the GIS may be referenced during profile analysis bysubmitting a query with metadata about entries in a location history(e.g., publisher, time of day, location, OS, device type, locationdetermination method (e.g. GPS vs. WiFi™ or other wireless network)).The reliability database may provide a response indicative of thepredicted level of accuracy of the incoming location. The reliabilitydatabase may store data from sources know to be reliable, and this datamay indicate expected levels of activity at a location. If a specificdata set diverges significantly from this (e.g., attribute scores for atile are more than a threshold amount different from those in thereliability database), in response, the user profiler may flag thelocation history as likely being less accurate, and based on such flags,the data may be discarded or changes to user profiles may be downweighted.

Upon determining that the location-attribute score is not an outlier,the process 40 proceeds to step 60, and a mean user-attribute score isupdated based on the location-attribute score. Updating theuser-attribute score may include updating each of a plurality ofuser-attribute scores based on a plurality of location-attribute scoresthat were not filtered out in step 58. Updating the user-attribute scoremay include multiplying the current score by a count of measurementsupon which that score is based, adding to the resulting product thelocation-attribute score, and dividing this some by the count plus oneto calculate a new average user-attribute score. This value andincremented version of the count may be stored in a correspondingattribute record in the user profile.

The process 40 may be repeated for each of a relatively large number oflocation histories, each location history corresponding to a differentuser profile. The process 40 may be repeated periodically, for examplenightly, weekly, or monthly to update user profiles and instantiate newuser profiles. The resulting user profiles may be stored in theabove-mentioned user-profile datastore 14.

In some cases, after updating user profiles, various indices may beformed to expedite certain queries to the geographic information system18. For example, some embodiments may form an index keyed to eachattribute for which a score is maintained in the tile records or theuser-profile records. For example, embodiments may calculate an indexthat identifies each tile in which users having more than a thresholdscore for a given attribute co-occur during one of the above-describedtime bins (e.g., by multiplying a density score for each tile with anattribute score and thresholding the resulting product). This index maybe used to relatively quickly determine whether a given geographic areaat a given time is correlated with a given attribute and has a highdensity of people exhibiting behavior described by that attribute.Further, some embodiments may use such an index to identify geographicareas in which a collection of attributes are relatively strong, forinstance determining the union of the set of values corresponding toeach of a plurality of different attributes to identify, for instance,where users associated with golfing, fishing, and tourism are at arelatively high concentration on mid-afternoons on Sundays.

Embodiments of the process 40 may be performed concurrently on multiplecomputing devices to expedite calculations. For instance, a mastercomputing device may iterate through a list of user device identifiersand assign profiling tasks to each of a plurality of profiling computingdevices, each of which determine corresponding profiles for differentusers at the same time. Using similar techniques, the formation ofindices may also be parallelized. For instance, each attribute may beassigned to a different set of computing devices, and that set ofcomputing devices may identify the areas in which the attribute hascertain criteria (e.g., greater than a threshold amount of activity),while other sets of computing devices perform the same task fordifferent attributes. Such concurrent operations are expected tofacilitate faster computation of profiles and indices than wouldotherwise be achieved, though not all embodiments provide for concurrentoperations.

Other uses of concurrency may expedite data retrieval. For instance,querying a GIS once for each user event (e.g., a particular user beingpresent in a tile at during a particular window of time) may berelatively slow, as the number of such events can be very large. Toexpedite retrieval from the GIS, some embodiments may group events toreduce queries. Such embodiments may include a master computing device(e.g., a virtualized or physical computing instance) that maps each tileto one of a plurality of other computing devices (e.g., a virtualized orphysical computing instance) and instructs those devices (e.g., over alocal area network in a data center) to gather data from the GIS aboutevents occurring in their respective assigned tile or tiles. Inresponse, the other computing devices may each filter the user eventsoccurring within their respective tile from the obtained locationhistories, each forming an event group of events occurring within anassigned tile, and submit one or more queries to the GIS for attributesof the tile during relevant time periods corresponding to user events inthe group (e.g., when a user was in the tile). After the responsive datais retrieved, the other computing devices may then iterate through eachuser having user events in the event group and join the responsive GISdata for each user with the corresponding user profile. Thus a singlequery, or one query per time period in question, may retrieve relevantdata for a plurality of user events, thereby reducing the number ofqueries to the GIS and expediting analysis of user histories. Further,parallelizing the analysis for different tiles across multiple computingdevices is expected to further expedite such analyses.

Further, in the above example of concurrent operation in which differenttiles are assigned to different computing devices, each of the othercomputing devices holds in memory user profiles for users passingthrough the tile. This user profile data may be aggregated to calculateor update a count for the tile at a particular window of time, e.g., bycounting the number of user profiles corresponding to the tile at aparticular time and having a particular attribute, such as an attributescore greater than a threshold. Again, concurrent operation is expectedto expedite analysis, and aggregating the user profile datacorresponding to the respective tiles while this data is in memory forpurposes of updating the user profiles is expected to reduce calls tothe user-profile data store and speed analysis.

In some embodiments, analysis of user profiles is parallelized accordingto the combination of user profile and attribute, such that differentcomputing devices analyze different attributes for a given userconcurrently and different computing devices analyze different usersconcurrently (e.g., mapping user A, attribute X to device 1; user A,attribute Y to device 2; user B, attribute X to device 3; etc.). Again,a master computing device may map profile-attribute pairs to each of aplurality of other computing devices and instruct those devices (e.g.,over a local area network in a data center) to sum the counts for thoseattributes for those users across all of the tiles having data for thoseusers. For instance, the above technique may be used to analyze each ofa plurality of tiles concurrently with different computing devicesmapped to different tiles, and then the technique of this paragraph maybe used to aggregate this data for each user profile/attribute pairacross the tiles, e.g., by querying the devices analyzing tiles for dataabout a given user and attribute and summing (or otherwise aggregating)the responses. Again, this technique is expected to offer relativelyfast concurrent operation and reduce calls to data stores that mightotherwise slow the analysis.

The user-profiles resulting from the above describe processes andsystems may be used in a variety of contexts. For example, as notedabove, advertisements may be selected based on the user profiles. Inanother example, the user profiles may be used to do market research,for example, by identifying which attributes score relatively high ateach of a business's locations at certain times to characterize thecustomers of that business. In another example, the user profiles may beused to customize the look and feel or operation of software operated bythe user, for instance configuring application differently for a userknown to have children relative to the look, feel, or operationpresented to a user who has attribute scores that indicate that userlikely does not have children.

Thus, the above-describes processes may yield user profiles in anautomated fashion, at relatively low expense, and in a privacy friendlymanner. Associating attributes of geographic locations visited by theuser to the user's profile, and accounting for the time of day at thegeographic location, and for the user, are expected to yield relativelyaccurate user profiles that account for the different ways people behaveduring different times of the day. Further, inferring intermediatelocations is expected to yield a relatively high resolutioncharacterization of users, and determining whether the user is at anadjacent location, whether adjacent locations have consistentattributes, and whether the attributes of a given location are outliersfor the user are expected to further improve the quality of the userprofile. It should be noted, however, that not all embodiments providethese benefits.

FIG. 4 is a diagram that illustrates an exemplary computing system 1000in accordance with embodiments of the present technique. Variousportions of systems and methods described herein, may include or beexecuted on one or more computer systems similar to computing system1000. Further, processes and modules described herein may be executed byone or more processing systems similar to that of computing system 1000.

Computing system 1000 may include one or more processors (e.g.,processors 1010 a-1010 n) coupled to system memory 1020, an input/outputI/O device interface 1030 and a network interface 1040 via aninput/output (I/O) interface 1050. A processor may include a singleprocessor or a plurality of processors (e.g., distributed processors). Aprocessor may be any suitable processor capable of executing orotherwise performing instructions. A processor may include a centralprocessing unit (CPU) that carries out program instructions to performthe arithmetical, logical, and input/output operations of computingsystem 1000. A processor may execute code (e.g., processor firmware, aprotocol stack, a database management system, an operating system, or acombination thereof) that creates an execution environment for programinstructions. A processor may include a programmable processor. Aprocessor may include general or special purpose microprocessors. Aprocessor may receive instructions and data from a memory (e.g., systemmemory 1020). Computing system 1000 may be a uni-processor systemincluding one processor (e.g., processor 1010 a), or a multi-processorsystem including any number of suitable processors (e.g., 1010 a-1010n). Multiple processors may be employed to provide for parallel orsequential execution of one or more portions of the techniques describedherein. Processes, such as logic flows, described herein may beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating corresponding output. Processes described herein may beperformed by, and apparatus can also be implemented as, special purposelogic circuitry, e.g., an FPGA (field programmable gate array) or anASIC (application specific integrated circuit). Computing system 1000may include a plurality of computing devices (e.g., distributed computersystems) to implement various processing functions.

I/O device interface 1030 may provide an interface for connection of oneor more I/O devices 1060 to computer system 1000. I/O devices mayinclude devices that receive input (e.g., from a user) or outputinformation (e.g., to a user). I/O devices 1060 may include, forexample, graphical user interface presented on displays (e.g., a cathoderay tube (CRT) or liquid crystal display (LCD) monitor), pointingdevices (e.g., a computer mouse or trackball), keyboards, keypads,touchpads, scanning devices, voice recognition devices, gesturerecognition devices, printers, audio speakers, microphones, cameras, orthe like. I/O devices 1060 may be connected to computer system 1000through a wired or wireless connection. I/O devices 1060 may beconnected to computer system 1000 from a remote location. I/O devices1060 located on remote computer system, for example, may be connected tocomputer system 1000 via a network and network interface 1040.

Network interface 1040 may include a network adapter that provides forconnection of computer system 1000 to a network. Network interface may1040 may facilitate data exchange between computer system 1000 and otherdevices connected to the network. Network interface 1040 may supportwired or wireless communication. The network may include an electroniccommunication network, such as the Internet, a local area network (LAN),a wide area (WAN), a cellular communications network or the like.

System memory 1020 may be configured to store program instructions 1100or data 1110. Program instructions 1100 may be executable by a processor(e.g., one or more of processors 1010 a-1010 n) to implement one or moreembodiments of the present techniques. Instructions 1100 may includemodules of computer program instructions for implementing one or moretechniques described herein with regard to various processing modules.Program instructions may include a computer program (which in certainforms is known as a program, software, software application, script, orcode). A computer program may be written in a programming language,including compiled or interpreted languages, or declarative orprocedural languages. A computer program may include a unit suitable foruse in a computing environment, including as a stand-alone program, amodule, a component, a subroutine. A computer program may or may notcorrespond to a file in a file system. A program may be stored in aportion of a file that holds other programs or data (e.g., one or morescripts stored in a markup language document), in a single filededicated to the program in question, or in multiple coordinated files(e.g., files that store one or more modules, sub programs, or portionsof code). A computer program may be deployed to be executed on one ormore computer processors located locally at one site or distributedacross multiple remote sites and interconnected by a communicationnetwork.

System memory 1020 may include a tangible program carrier having programinstructions stored thereon. A tangible program carrier may include anon-transitory computer readable storage medium. A non-transitorycomputer readable storage medium may include a machine readable storagedevice, a machine readable storage substrate, a memory device, or anycombination thereof. Non-transitory computer readable storage medium mayinclude, non-volatile memory (e.g., flash memory, ROM, PROM, EPROM,EEPROM memory), volatile memory (e.g., random access memory (RAM),static random access memory (SRAM), synchronous dynamic RAM (SDRAM)),bulk storage memory (e.g., CD-ROM and/or DVD-ROM, hard-drives), or thelike. System memory 1020 may include a non-transitory computer readablestorage medium may have program instructions stored thereon that areexecutable by a computer processor (e.g., one or more of processors 1010a-1010 n) to cause the subject matter and the functional operationsdescribed herein. A memory (e.g., system memory 1020) may include asingle memory device and/or a plurality of memory devices (e.g.,distributed memory devices). In some embodiments, the program may beconveyed by a propagated signal, such as a carrier wave or digitalsignal conveying a stream of packets.

I/O interface 1050 may be configured to coordinate I/O traffic betweenprocessors 1010 a-1010 n, system memory 1020, network interface 1040,I/O devices 1060 and/or other peripheral devices. I/O interface 1050 mayperform protocol, timing or other data transformations to convert datasignals from one component (e.g., system memory 1020) into a formatsuitable for use by another component (e.g., processors 1010 a-1010 n).I/O interface 1050 may include support for devices attached throughvarious types of peripheral buses, such as a variant of the PeripheralComponent Interconnect (PCI) bus standard or the Universal Serial Bus(USB) standard.

Embodiments of the techniques described herein may be implemented usinga single instance of computer system 1000, or multiple computer systems1000 configured to host different portions or instances of embodiments.Multiple computer systems 1000 may provide for parallel or sequentialprocessing/execution of one or more portions of the techniques describedherein.

Those skilled in the art will appreciate that computer system 1000 ismerely illustrative and is not intended to limit the scope of thetechniques described herein. Computer system 1000 may include anycombination of devices or software that may perform or otherwise providefor the performance of the techniques described herein. For example,computer system 1000 may include or be a combination of acloud-computing system, a data center, a server rack, a server, avirtual server, a desktop computer, a laptop computer, a tabletcomputer, a server device, a client device, a mobile telephone, apersonal digital assistant (PDA), a mobile audio or video player, a gameconsole, a vehicle-mounted computer, or a Global Positioning System(GPS), or the like. Computer system 1000 may also be connected to otherdevices that are not illustrated, or may operate as a stand-alonesystem. In addition, the functionality provided by the illustratedcomponents may in some embodiments be combined in fewer components ordistributed in additional components. Similarly, in some embodiments,the functionality of some of the illustrated components may not beprovided or other additional functionality may be available.

Those skilled in the art will also appreciate that, while various itemsare illustrated as being stored in memory or on storage while beingused, these items or portions of them may be transferred between memoryand other storage devices for purposes of memory management and dataintegrity. Alternatively, in other embodiments some or all of thesoftware components may execute in memory on another device andcommunicate with the illustrated computer system via inter-computercommunication. Some or all of the system components or data structuresmay also be stored (e.g., as instructions or structured data) on acomputer-accessible medium or a portable article to be read by anappropriate drive, various examples of which are described above. Insome embodiments, instructions stored on a computer-accessible mediumseparate from computer system 1000 may be transmitted to computer system1000 via transmission media or signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as a network or a wireless link. Various embodiments may furtherinclude receiving, sending or storing instructions or data implementedin accordance with the foregoing description upon a computer-accessiblemedium. Accordingly, the present invention may be practiced with othercomputer system configurations.

It should be understood that the description and the drawings are notintended to limit the invention to the particular form disclosed, but tothe contrary, the intention is to cover all modifications, equivalents,and alternatives falling within the spirit and scope of the presentinvention as defined by the appended claims. Further modifications andalternative embodiments of various aspects of the invention will beapparent to those skilled in the art in view of this description.Accordingly, this description and the drawings are to be construed asillustrative only and are for the purpose of teaching those skilled inthe art the general manner of carrying out the invention. It is to beunderstood that the forms of the invention shown and described hereinare to be taken as examples of embodiments. Elements and materials maybe substituted for those illustrated and described herein, parts andprocesses may be reversed or omitted, and certain features of theinvention may be utilized independently, all as would be apparent to oneskilled in the art after having the benefit of this description of theinvention. Changes may be made in the elements described herein withoutdeparting from the spirit and scope of the invention as described in thefollowing claims. Headings used herein are for organizational purposesonly and are not meant to be used to limit the scope of the description.

As used throughout this application, the word “may” is used in apermissive sense (i.e., meaning having the potential to), rather thanthe mandatory sense (i.e., meaning must). The words “include”,“including”, and “includes” and the like mean including, but not limitedto. As used throughout this application, the singular forms “a”, “an”and “the” include plural referents unless the content explicitlyindicates otherwise. Thus, for example, reference to “an element” or “aelement” includes a combination of two or more elements, notwithstandinguse of other terms and phrases for one or more elements, such as “one ormore.” The term “or” is, unless indicated otherwise, non-exclusive,i.e., encompassing both “and” and “or.” Terms describing conditionalrelationships, e.g., “in response to X, Y,” “upon X, Y,”, “if X, Y,”“when X, Y,” and the like, encompass causal relationships in which theantecedent is a necessary causal condition, the antecedent is asufficient causal condition, or the antecedent is a contributory causalcondition of the consequent, e.g., “state X occurs upon condition Yobtaining” is generic to “X occurs solely upon Y” and “X occurs upon Yand Z.” Such conditional relationships are not limited to consequencesthat instantly follow the antecedent obtaining, as some consequences maybe delayed, and in conditional statements, antecedents are connected totheir consequents, e.g., the antecedent is relevant to the likelihood ofthe consequent occurring. Further, unless otherwise indicated,statements that one value or action is “based on” another condition orvalue encompass both instances in which the condition or value is thesole factor and instances in which the condition or value is one factoramong a plurality of factors. Unless specifically stated otherwise, asapparent from the discussion, it is appreciated that throughout thisspecification discussions utilizing terms such as “processing”,“computing”, “calculating”, “determining” or the like refer to actionsor processes of a specific apparatus, such as a special purpose computeror a similar special purpose electronic processing/computing device. Inthe context of this specification, a special purpose computer or asimilar special purpose electronic processing or computing device iscapable of manipulating or transforming signals, for instance signalsrepresented as physical electronic, optical, or magnetic quantitieswithin memories, registers, or other information storage devices,transmission devices, or display devices of the special purpose computeror similar special purpose processing or computing device.

What is claimed is:
 1. A method of profiling a user of a mobile computing device, the method comprising: obtaining a location history of a user, the location history being based on signals from a mobile computing device of the user; obtaining a location-attribute score of a location identified in, or inferred from, the location history; determining, with a computer, a user-attribute score based on the location-attribute score; storing the user-attribute score in a user-profile datastore; and wherein, obtaining a location history of a user comprises: obtaining a plurality of pairs of geolocations and times at which the user was at the geolocation, the geolocation being determined based on a wireless environment of a hand-held computing device; obtaining a location-attribute score of a location comprises: mapping a location in the location history to a tile in a geographic information system (GIS), the tile being a quantization of a geographic area described by the GIS and including the location; and retrieving a list of tile-attribute scores for the tile from the GIS, the tile-attribute scores each describing a potential activity of a user while in the tile and a statistical likelihood that a user is engaging in the activity based on behavior of other users while in the tile; determining a user-attribute score comprises: for each tile-attribute score in the list of retrieved attribute scores, updating an average of scores for the respective attribute for the user based on scores of tiles corresponding to previous locations of the user; and storing the user-attribute score comprises: storing the updated averages for each attribute in a profile of the user in the user-profile datastore, wherein the user-profile datastore is operative to receive a request for attributes of a user from an advertiser, retrieve attributes of the user from a responsive user profile, and identify attributes of the responsive user to the advertiser to assist the advertiser with the selection of advertisements for the responsive user.
 2. The method of claim 1, wherein determining a user-attribute score comprises: determining a user-attribute score for a user-profile time-bin, the user profile being divided into a plurality of time-bins, with the same attribute having different values in different time-bins of the user profile, the different values corresponding to a propensity of the user to engage in an activity described by the attribute during a time within a given time-bin.
 3. The method of claim 1, wherein determining a user-attribute score comprises: identifying a time-bin of a profile of the user, the time-bin corresponding to a time at which the user was at the location, the user profile having a plurality of time-bins; and determining a user-attribute score corresponding to the identified time-bin.
 4. The method of claim 1, wherein obtaining a location-attribute score comprises: identifying a time-bin of a record in a GIS, the record corresponding to the location, and the time-bin corresponding to a time at which the user was at the location; and determining a user-attribute score based on an attribute score of the identified time-bin of the location.
 5. The method of claim 1, wherein the location-attribute score is a time-bin specific score for the location, and wherein the user-attribute score is a time-bin specific score for the user based on the location history.
 6. The method of claim 1, wherein each user is associated with a plurality of scores for the attribute, each score corresponding to a different duration of time during the week.
 7. The method of claim 1, wherein obtaining a location-attribute score of a location identified in, or inferred from, the location history comprises: identifying a first tile in a GIS, the first tile defining a geographic area including the location identified in the location history.
 8. The method of claim 7, wherein identifying a tile comprises: determining that the user is located in a second tile, adjacent the first tile, based on attribute scores of both the first tile and the second tile.
 9. The method of claim 1, wherein obtaining a location-attribute score of a location comprises: obtaining an initial location of the user at an initial time; obtaining a subsequent location of the user at a subsequent time, the subsequent time being more than one minute after the initial time; inferring an intermediate location of the user at an intermediate time based on attributes of locations between the initial location and the subsequent location; and obtaining a location-attribute score of the intermediate location.
 10. The method of claim 9, wherein the intermediate location is associated with a confidence score indicative of a likelihood that the user was at the intermediate location, and wherein the user attribute score is calculated by weighting the location-attribute score with the confidence score.
 11. The method of claim 1, wherein determining a user-attribute score based on the location-attribute score comprises: calculating a measure of central tendency using the location-attribute score and scores for the attribute from previous locations of the user.
 12. The method of claim 1, wherein determining a user-attribute score based on the location-attribute score comprises: determining that the location-attribute score is not an outlier relative to scores for the attribute from previous locations of the user; and in response, calculating an average of the location-attribute score and scores for the attribute from previous locations of the user.
 13. The method of claim 1, wherein determining a user-attribute score based on the location-attribute score comprises: determining that adjacent locations have attribute scores consistent with the location-attribute score; and in response, calculating the user-attribute score based on the location-attribute score.
 14. The method of claim 13, wherein determining that adjacent locations have attribute scores consistent with the location-attribute score comprises: determining more than a threshold amount of adjacent tiles have scores for the attribute within a threshold difference from the location-attribute score.
 15. The method of claim 1, comprising: determining user-attribute scores for a plurality of attributes of a plurality of users based on location histories of the plurality of users and attributes of locations identified by the location histories; and storing the user-attribute scores in a plurality of corresponding user profiles in the user-profile datastore.
 16. The method of claim 1, wherein: the location history comprises an list of geolocation records, each geolocation record including geographic coordinates expressed as a latitude and longitude and a time at which the mobile computing device was at the respective coordinates, each geolocation record being obtained by an end-user portable device having access to a location identifying service; obtaining a location-attribute score comprises: inferring locations between locations identified in the location history; for each identified or inferred location, retrieving respective tile records from a GIS, the tile records corresponding to a tile in which the respective location is disposed and adjacent tiles, each tile record corresponding to a geographic area of between 100 square meters and 100,000 square meters and being associated with one or more location-attribute scores, each location-attribute score corresponding to an activity of interest to advertisers and ordinal values indicative of a likelihood that a user is engaged in the respective activity in the tile during each of a plurality of time-bins, the time-bins defining different subsets of a week, including a weekday morning bin, a weekday lunch-time bin, and a weekend evening bin, and the attributes including the user being at work, the user engaging in sports, the user shopping, and the user engaging in tourism; determining a user-attribute score comprises: determining that location attribute scores for the tile records for the time-bin in which the user was at the location are consistent among the adjacent tiles; and in response, determining a plurality of user-attribute scores corresponding the location-attribute scores, the respective user-attribute score being an average of the corresponding location-attribute score for a time-bin including the time at which the user was a the location and previous scores for the attribute from other locations; and storing the user-attribute score comprises: storing the averaged user-attribute scores in a user profile in the user-profile datastore, user profile being stored on a tangible, non-transitory, machine-readable medium, and the user-profile datastore being operative to respond to queries from advertisers for data relevant to the selection of advertisements.
 17. The method of claim 1, comprising: receiving a request from an advertiser for data about a user, the request including a user identifier; determining that the advertiser is authorized to access the user-profile datastore; retrieving from a user profile responsive to the user identifier one or more attributes of the responsive user; and sending to the advertiser data indicative of the one or more attributes.
 18. The method of claim 1, comprising: obtaining user profiles of users at each of a plurality of locations corresponding to a business; and aggregating attributes of the user profiles to characterize the business.
 19. The method of claim 1, comprising: obtaining a user profile of a user operating an application; and customizing the application based on the user profile.
 20. A tangible, machine-readable, non-transitory medium storing instructions that when executed by a data processing apparatus, cause the data processing apparatus to perform operations comprising: obtaining a location history of a user, the location history being based on signals from a mobile computing device of the user; obtaining a location-attribute score of a location identified in, or inferred from, the location history; determining a user-attribute score based on the location-attribute score; and storing the user-attribute score in a user-profile datastore and wherein, obtaining a location history of a user comprises: obtaining a plurality of pairs of geolocations and times at which the user was at the geolocation, the geolocation being determined based on a wireless environment of a hand-held computing device; obtaining a location-attribute score of a location comprises: mapping a location in the location history to a tile in a geographic information system (GIS), the tile being a quantization of a geographic area described by the GIS and including the location; and retrieving a list of tile-attribute scores for the tile from the GIS, the tile-attribute scores each describing a potential activity of a user while in the tile and a statistical likelihood that a user is engaging in the activity based on behavior of other users while in the tile; determining a user-attribute score comprises: for each tile-attribute score in the list of retrieved attribute scores, updating an average of scores for the respective attribute for the user based on scores of tiles corresponding to previous locations of the user; and storing the user-attribute score comprises: storing the updated averages for each attribute in a profile of the user in the user-profile datastore, wherein the user-profile datastore is operative to receive a request for attributes of a user from an advertiser, retrieve attributes of the user from a responsive user profile, and identify attributes of the responsive user to the advertiser to assist the advertiser with the selection of advertisements for the responsive user.
 21. A system, comprising: one or more processors; and memory storing instructions that when executed by the processors cause the processors to perform operations comprising: obtaining a location history of a user, the location history being based on signals from a mobile computing device of the user; obtaining a location-attribute score of a location identified in, or inferred from, the location history; determining a user-attribute score based on the location-attribute score; and storing the user-attribute score in a user-profile datastore; and wherein, obtaining a location history of a user comprises: obtaining a plurality of pairs of geolocations and times at which the user was at the geolocation, the geolocation being determined based on a wireless environment of a hand-held computing device; obtaining a location-attribute score of a location comprises: mapping a location in the location history to a tile in a geographic information system (GIS), the tile being a quantization of a geographic area described by the GIS and including the location; and retrieving a list of tile-attribute scores for the tile from the GIS, the tile-attribute scores each describing a potential activity of a user while in the tile and a statistical likelihood that a user is engaging in the activity based on behavior of other users while in the tile; determining a user-attribute score comprises: for each tile-attribute score in the list of retrieved attribute scores, updating an average of scores for the respective attribute for the user based on scores of tiles corresponding to previous locations of the user; and storing the user-attribute score comprises: storing the updated averages for each attribute in a profile of the user in the user-profile datastore, wherein the user-profile datastore is operative to receive a request for attributes of a user from an advertiser, retrieve attributes of the user from a responsive user profile, and identify attributes of the responsive user to the advertiser to assist the advertiser with the selection of advertisements for the responsive user. 